The possibility of automatically classifying high frequency sub-bottom acoustic reflections collected from an Autonomous Underwater Robot is investigated in this paper. In field surveys of Cobalt-rich Manganese Crusts (Mn-crusts), existing methods relies on visual confirmation of seafloor from images and thickness measurements using the sub-bottom probe. Using these visual classification results as ground truth, an autoencoder is trained to extract latent features from bundled acoustic reflections. A Support Vector Machine classifier is then trained to classify the latent space to idetify seafloor classes. Results from data collected from seafloor at 1500m deep regions of Mn-crust showed an accuracy of about 70%.

翻译：本文研究了利用自主水下机器人采集的高频浅地层声学反射信号进行自动分类的可行性。在富钴结壳调查中，现有方法依赖海底图像目视确认及浅地层探头的厚度测量。以这些视觉分类结果作为基准，训练自编码器从捆绑声波反射中提取潜在特征，随后训练支持向量机分类器对潜在空间进行识别以实现海床类别划分。基于水深1500米富钴结壳区域海底采集数据的实验结果显示，分类准确率达到约70%。